Machine Learning for Predicting Claims Fraud in Auto Insurance

Abstract

Fraudulent claims in auto insurance are a persistent challenge, costing insurers billions of dollars annually and leading to higher premiums for honest policyholders. This study uses machine learning techniques to effectively predict and mitigate claims fraud. By leveraging historical claims data, we identify patterns and anomalies that signal fraudulent activities, empowering insurers to make informed decisions. The research evaluates the performance of algorithms such as decision trees, random forests, gradient boosting, and neural networks in detecting fraud, focusing on their accuracy, scalability, and interpretability. Feature engineering plays a crucial role, with key variables including claim amounts, accident descriptions, policyholder demographics, and historical claim behaviours. Through a robust validation process using real-world insurance datasets, the findings reveal that machine learning models can significantly outperform traditional rule-based systems in identifying fraudulent claims. Moreover, the study highlights the importance of balancing predictive power with fairness, ensuring models do not discriminate against genuine claimants inadvertently. Practical implications include Reducing the time and resources spent on manual investigations, Enhancing fraud detection accuracy & Improving overall customer experience. This research underscores the potential of data-driven approaches to transform fraud management in auto insurance. These approaches could pave the way for more efficient and secure operations while promoting trust and fairness in the industry.